Great Games
2022-11-11
1 Overview
Our research question is: how to audit whether labels are “conceptualized consistently” across different countries and different languages. We propose a framework to audit labels themselves for world-wide inclusivity. Here, we are not concerned with a specific ML implementation, and instead audit the labels that are being fed into systems.
Figure 1 is descriptive statistics of our survey.
- We consider country comparisons in three ways:
- Within each country, how much “internal” agreement is there on labels of interest? (Figure 2a)
- How do different populations view these labels in specific contexts? (Figure2b)
- If label outcome differences are found across countries, is there a data-backed explanation relating to culture? (Figure3)
- We consider language comparison in three ways:
- How similar are label choices for bilingual speakers when asked questions in their native language versus English? (Figure4)
- How similar are label choices for English-speakers across English-speaking countries? (Figure5)
- How similar are label choices for speakers of non-English language? (Figure6)
Figure 5 and 6 should look similar to Figure 2b using similar approach and have not been plotted currently in this doc.
bookdown::serve_book()2 Figure 1: Discriptive Statistics
source("read.R")
head(df)## # A tibble: 6 × 17
## Response.ID game country GENDER AGE ageCut native Langu…¹ region exper…²
## <chr> <chr> <fct> <chr> <int> <fct> <fct> <chr> <fct> <dbl>
## 1 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 2 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 3 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 4 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 5 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 6 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## # … with 7 more variables: hardcore <fct>, accessibility <fct>, labeler <chr>,
## # ambassador <chr>, label <fct>, answer <int>, gender <chr>, and abbreviated
## # variable names ¹Language, ²experience
## # ℹ Use `colnames()` to see all variable names2.1 Participant breakdown by gender, ageCut, hardcore, accessibility, country, game
# 5,991 participants in total
nrow(df %>% distinct(Response.ID))## [1] 5991# gender, age, experience, accessibility (disability) breakdowns
# gender: Male, Female, and Nonbinary.
df %>% distinct(gender, Response.ID) %>% count(gender)## # A tibble: 3 × 2
## gender n
## <chr> <int>
## 1 Female 432
## 2 Male 5497
## 3 NonBinary 62# ageCut: 18-24, 25-34, 35-44, 45-55, 55+. Per Xbox internal age breakdowns.
df %>% distinct(ageCut, Response.ID) %>% count(ageCut)## # A tibble: 6 × 2
## ageCut n
## <fct> <int>
## 1 18-24 1182
## 2 25-34 1620
## 3 35-44 1641
## 4 45-55 773
## 5 55+ 111
## 6 <NA> 664# hardcore: hardcore (play games over 1 time a week), non-hardcore (play games 1 time or less)
df %>% distinct(hardcore, Response.ID) %>% count(hardcore)## # A tibble: 2 × 2
## hardcore n
## <fct> <int>
## 1 hardcore 5608
## 2 non-hardcore 383# accessibility: or really disability, Yes/No. Those people who incated that they have disability or not
df %>% distinct(accessibility, Response.ID) %>% count(accessibility)## # A tibble: 4 × 2
## accessibility n
## <fct> <int>
## 1 No 5466
## 2 Prefer not to answer 112
## 3 Yes 328
## 4 <NA> 85# countries
df %>% distinct(country, Response.ID) %>% count(country)## # A tibble: 16 × 2
## country n
## <fct> <int>
## 1 US 3040
## 2 Germany 245
## 3 Poland 123
## 4 Greece 319
## 5 Japan 237
## 6 Korea 165
## 7 Singapore 69
## 8 India 99
## 9 Saudi.Arabia 35
## 10 South.Africa 212
## 11 Nigeria 170
## 12 Brazil 280
## 13 Argentina 182
## 14 Colombia 165
## 15 Chile 193
## 16 Mexico 457# games
df %>% distinct(game, Response.ID) %>% count(game)## # A tibble: 11 × 2
## game n
## <chr> <int>
## 1 Animal Crossing: New Horizons 1102
## 2 Call of Duty: Vanguard 1718
## 3 Candy Crush 799
## 4 Elden Ring 1896
## 5 FIFA22 1123
## 6 Grand Theft Auto V 3168
## 7 Mario Kart 8 1651
## 8 Minecraft 2631
## 9 PUBG 1241
## 10 Stardew Valley 1043
## 11 The Sims 3 4292.2 Label correlations
- In this plot, we look at correlation between labels from our sample. We can observe that difficulty, violent, action, action.motivation, control_complexity, strategy, learning_curve tend to go together. Comedic, creativity, pacifist, zen,made.for.kids, cozy are clustered together.
library(corrplot)## corrplot 0.92 loaded# disregard the NA
res <- df %>%
filter(!(label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art"))) %>%
mutate(label=factor(label, levels=unique(df$label))) %>% # keep order in the final graph Figure 1
select(Response.ID, label, answer) %>%
pivot_wider(., names_from = label, values_from = answer) %>%
unnest() %>%
select(-Response.ID)
mx <- cor(res, use = "complete.obs")
corrplot(mx, type = "upper", order = "FPC", tl.cex=2,
tl.col = "black", tl.srt = 45, title="Figure 1: Label Correlation")
3 Figure 2a: Consider country comparisons in three ways
This step is optional. We remove games where a country has very few response (< 5). In other words, we find games with >=5 participants across all countries.
# filter out countries with few data point
# for each game, remove countries with few data points
constraints <- df %>% count(game, country, label) %>% # group by country and label
select(game, country, n) %>% distinct() %>%
filter(n < 5)
# remove games that have too few response in any country
df <- df %>% filter(!(game %in% unique(constraints$game))) %>%
filter(!(label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art")))
# game included
unique(df$game)## [1] "PUBG" "Grand Theft Auto V" "Minecraft"
## [4] "Elden Ring" "FIFA22" "Call of Duty: Vanguard"3.1 Within each country, how much “internal” agreement is there on labels of interest?
We seek to answer questions such as does the concept of a “cozy game” elicit more agreement within the US than it does in Japan? To achieve so, we calculate the average standard deviation/variance of label outcomes and comparing across countries. We plot standard deviation to reduce the length of this document.
Code Description: plotFigure2a is a wrapper function that plots all 28 label output. The first three are 1-3 scale, 1-4 scale for difficulty the latter 24 are 0-1 scale. plotVariance is the plotting function.
# wrapper function that enable individual game
plotFigure2a <- function(game = "All", sd=FALSE, debug=FALSE) {
var.df <- df %>% group_by(game, country, label) %>%
summarise(mean_answer = mean(answer, na.rm=TRUE),
variance = var(answer, na.rm=TRUE),
sd=sd(answer, na.rm=TRUE))
if(game == "All" && !sd) {
# average the variances across games for each country
country.df <- var.df %>% group_by(country, label) %>%
summarise(y=mean(variance, na.rm=TRUE))
} else if (game == "All" && sd) {
country.df <- var.df %>% group_by(country, label) %>%
summarise(y=mean(sd, na.rm=TRUE))
} else if(game != "All" && !sd) {
country.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=variance)
} else if (game != "All" && sd) {
country.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=sd)
} else {
print("Error...")
}
p1 <- plotVariance(country.df, gameTitle=game, sd=sd, binary=FALSE)
p2 <- plotVariance(country.df, gameTitle=game, sd=sd, binary=TRUE)
res <- ggarrange(p1, p2, ncol=1, heights = c(1, 6))
return(res)
}
# plot variance of a label across countries
plotVariance <- function(inputDf, gameTitle="All", sd=FALSE, binary=FALSE) {
# color the US
inputDf$color <- inputDf$country == "US"
plotDf <- inputDf[with(inputDf, order(label, country, y)),] %>%
filter(!label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
if(!binary) {
plotDf <- inputDf[with(inputDf, order(label, country, y)),] %>%
filter(label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
}
if(sd) {
yLabel <- "Standard Devaition"
} else {
yLabel <- "Variance"
}
p <- plotDf %>% ggplot(aes(x=reorder_within(country, y, label), y=y, color=color)) +
geom_point() +
scale_x_reordered() +
facet_wrap(~label, ncol=4, scales = "free") +
ylim(c(0, 3)) +
scale_color_manual(values=c("#999999", "#56B4E9")) +
labs(y = yLabel, x = NULL,
title = paste0("How much internal agreement is there on labels of interest (", gameTitle, ")"))
if(binary) {
p <- p + ylim(c(0, 1))
}
return(p)
}3.1.1 Taken all games together.
Here we look at the standard deviation across all the games. We look at all the games together. (e.g., Does the concept of a “cozy game” elicit more agreement within the US than it does in Japan regardless of games?) The US is highlighted in the graph in Figure 2a.
We can observe that Saudi Arabia with the highest variability (i.e., standard deviation) 11 times, South Africa 2 times, Nigeria 7 times, Singapore 4 times, Japan 1 time, India 1 time, Mexico 1 time, and Colombia 1 time.
plotFigure2a("All", sd=TRUE, debug=FALSE)
3.1.2 Breakdown by game
Here we look at the variance/standard deviation in individual games. We look at all the games together. (e.g., Does the concept of a “cozy game” elicit more agreement within the US than it does in Japan in specific games?)
Note that not in one time did the US has the highest standard deviation.
3.1.2.1 PUBG
plotFigure2a("PUBG", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.2 Grand Theft Auto V
plotFigure2a("Grand Theft Auto V", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.3 Minecraft
plotFigure2a("Minecraft", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.4 Elden Ring
plotFigure2a("Elden Ring", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.5 FIFA22
plotFigure2a("FIFA22", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.6 Call of Duty: Vanguard
plotFigure2a("Call of Duty: Vanguard", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.2 Within each region, how much “internal” agreement is there on labels of interest?
In this case, we group countries to higher-level regions based on the World Value Survey map. We perform the same analysis but on a regional level.
# wrapper function that enable individual game
plotFigure2aRegion <- function(game = "All", sd=FALSE, debug=FALSE) {
var.df <- df %>% group_by(game, region, label) %>%
summarise(mean_answer = mean(answer, na.rm=TRUE),
variance = var(answer, na.rm=TRUE),
sd=sd(answer, na.rm=TRUE))
if(game == "All" && !sd) {
# average the variances across games for each region
region.df <- var.df %>% group_by(region, label) %>%
summarise(y=mean(variance, na.rm=TRUE))
} else if (game == "All" && sd) {
region.df <- var.df %>% group_by(region, label) %>%
summarise(y=mean(sd, na.rm=TRUE))
} else if(game != "All" && !sd) {
region.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=variance)
} else if (game != "All" && sd) {
region.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=sd)
} else {
print("Error...")
}
p1 <- plotVariance(region.df, gameTitle=game, sd=sd, binary=FALSE)
p2 <- plotVariance(region.df, gameTitle=game, sd=sd, binary=TRUE)
res <- ggarrange(p1, p2, ncol=1, heights = c(1, 6))
return(res)
}
# plot variance of a label across countries
plotVariance <- function(inputDf, gameTitle="All", sd=FALSE, binary=FALSE) {
# color the US
inputDf$color <- inputDf$region == "US"
plotDf <- inputDf[with(inputDf, order(label, region, y)),] %>%
filter(!label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
if(!binary) {
plotDf <- inputDf[with(inputDf, order(label, region, y)),] %>%
filter(label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
}
if(sd) {
yLabel <- "Standard Devaition"
} else {
yLabel <- "Variance"
}
p <- plotDf %>% ggplot(aes(x=reorder_within(region, y, label), y=y, color=color)) +
geom_point() +
scale_x_reordered() +
facet_wrap(~label, ncol=4, scales = "free") +
ylim(c(0, 3)) +
scale_color_manual(values=c("#999999", "#56B4E9")) +
labs(y = yLabel, x = NULL,
title = paste0("How much internal agreement is there on labels of interest (", gameTitle, ")"))
if(binary) {
p <- p + ylim(c(0, 1))
}
return(p)
}
4 Figure 2b: How do different populations view these labels in specific contexts?
To answer this question, we calculate differences in estimated mean label outcomes for each demographic using causal matching analysis. To predict the probability of having a game label in a certain country, we use multilevel regression and post-stratification (MRP).
# Import libraries
source("read.R")
library(ggalt)
library(reshape2)
options(dplyr.summarise.inform = FALSE)4.1 Calculate differences in estimated mean label outcomes for each demographic using causal matching analysis
We first check the differences between means for each labels across countries below. We run the following analyzeCountry function to each label and explores whether there is a significant difference between US and non-US participants in our matched dataframe.
analyzeCountry <- function(inputDf, label, lower=1, upper=3) {
df <- inputDf %>% filter(label == {{ label }}) %>%
mutate(is_us = ifelse(country == "US", 1, 0)) %>%
mutate(gender.no.nonbinary = ifelse(gender == "Male", 1, 0)) %>% # depending on how we want to deal with this
mutate(gender.no.nonbinary = factor(gender.no.nonbinary)) %>%
na.omit()
# causal matching using matchit
m.out <- matchit(is_us ~ game + ageCut + hardcore + gender.no.nonbinary, method = "nearest", distance = "mahalanobis", link = "probit", replace = TRUE, data=df)
# optional for debugging
s.out <- summary(m.out, standardize = TRUE)
plot(s.out)
matched.df <- match.data(m.out)
matched.df$is_us <- factor(matched.df$is_us)
model <- lm(answer ~ is_us, data=matched.df)
print(summary(model))
annotator <- df %>% filter(labeler == "Yes" & label == {{ label }}) %>%
count(game, answer) %>%
group_by(game) %>%
summarize(majority.vote = mean(answer[which(n==max(n))])) %>% ungroup()
p <- plotByGame(matched.df, "is_us", label, LOWER=lower, UPPER = upper) +
geom_point(aes(x=game, y=majority.vote, color="Polish Annotator", shape="Polish Annotator"), size=2, data=annotator) + # majority.vote is the Polish annotator majority vote
labs(title=paste0(label, " Score by Country"), y="Mean Score", x="Games") +
scale_color_discrete("Country")
return(p)
}- In the remainder of this section, we look at each individual label. For each label, we can look at whether there is a significant difference between US and non-US participants. For example, learning curve, replaybility, zen, space, violent, action, comedic, grinding, anime, motivation:action, motivation:social, motivation:immersion are significantly different.
4.1.1 Control Complexity
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6644 -0.6257 0.3356 0.3743 1.3743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.66443 0.05624 29.597 <2e-16 ***
## is_us1 -0.03875 0.05673 -0.683 0.495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6865 on 8669 degrees of freedom
## Multiple R-squared: 5.384e-05, Adjusted R-squared: -6.151e-05
## F-statistic: 0.4667 on 1 and 8669 DF, p-value: 0.4945
4.1.2 Learning Curve
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7651 -0.6227 -0.6227 0.3773 1.3773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.76510 0.06333 27.871 <2e-16 ***
## is_us1 -0.14236 0.06388 -2.228 0.0259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7731 on 8669 degrees of freedom
## Multiple R-squared: 0.0005725, Adjusted R-squared: 0.0004572
## F-statistic: 4.966 on 1 and 8669 DF, p-value: 0.02588
4.1.3 Difficulty
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90894 -0.90894 0.09106 0.10067 2.10067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.899329 0.076427 24.852 <2e-16 ***
## is_us1 0.009613 0.077092 0.125 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9329 on 8669 degrees of freedom
## Multiple R-squared: 1.793e-06, Adjusted R-squared: -0.0001136
## F-statistic: 0.01555 on 1 and 8669 DF, p-value: 0.9008
4.1.4 Replayability
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4363 -0.4363 0.5637 0.5637 0.7047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.29530 0.05902 38.892 <2e-16 ***
## is_us1 0.14098 0.05953 2.368 0.0179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7204 on 8669 degrees of freedom
## Multiple R-squared: 0.0006465, Adjusted R-squared: 0.0005313
## F-statistic: 5.608 on 1 and 8669 DF, p-value: 0.0179
4.1.5 Pacifist
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2013 -0.1641 -0.1641 -0.1641 0.8359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20134 0.03039 6.626 3.65e-11 ***
## is_us1 -0.03730 0.03065 -1.217 0.224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3709 on 8669 degrees of freedom
## Multiple R-squared: 0.0001708, Adjusted R-squared: 5.544e-05
## F-statistic: 1.481 on 1 and 8669 DF, p-value: 0.2237
4.1.6 Made for kids
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3557 -0.3202 -0.3202 0.6798 0.6798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35570 0.03824 9.301 <2e-16 ***
## is_us1 -0.03547 0.03858 -0.920 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4668 on 8669 degrees of freedom
## Multiple R-squared: 9.754e-05, Adjusted R-squared: -1.781e-05
## F-statistic: 0.8456 on 1 and 8669 DF, p-value: 0.3578
4.1.7 Cozy
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3044 -0.3044 -0.3044 0.6956 0.7248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27517 0.03768 7.302 3.08e-13 ***
## is_us1 0.02922 0.03801 0.769 0.442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.46 on 8669 degrees of freedom
## Multiple R-squared: 6.817e-05, Adjusted R-squared: -4.718e-05
## F-statistic: 0.591 on 1 and 8669 DF, p-value: 0.4421
4.1.8 Zen
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2773 -0.2773 -0.2773 0.7227 0.7920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20805 0.03662 5.681 1.38e-08 ***
## is_us1 0.06923 0.03694 1.874 0.061 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.447 on 8669 degrees of freedom
## Multiple R-squared: 0.000405, Adjusted R-squared: 0.0002897
## F-statistic: 3.512 on 1 and 8669 DF, p-value: 0.06096
4.1.9 Fantasy
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3340 -0.3340 -0.3340 0.6660 0.6711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32886 0.03864 8.511 <2e-16 ***
## is_us1 0.00510 0.03898 0.131 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4716 on 8669 degrees of freedom
## Multiple R-squared: 1.975e-06, Adjusted R-squared: -0.0001134
## F-statistic: 0.01712 on 1 and 8669 DF, p-value: 0.8959
4.1.10 Space
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08054 -0.01877 -0.01877 -0.01877 0.98123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08054 0.01141 7.061 1.78e-12 ***
## is_us1 -0.06176 0.01150 -5.368 8.15e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1392 on 8669 degrees of freedom
## Multiple R-squared: 0.003313, Adjusted R-squared: 0.003198
## F-statistic: 28.82 on 1 and 8669 DF, p-value: 8.151e-08
4.1.11 Heroic
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2537 -0.2537 -0.2537 0.7463 0.8054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19463 0.03560 5.467 4.69e-08 ***
## is_us1 0.05907 0.03591 1.645 0.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4345 on 8669 degrees of freedom
## Multiple R-squared: 0.000312, Adjusted R-squared: 0.0001967
## F-statistic: 2.706 on 1 and 8669 DF, p-value: 0.1
4.1.12 Real World
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3624 -0.3545 -0.3545 0.6455 0.6455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.362416 0.039197 9.246 <2e-16 ***
## is_us1 -0.007922 0.039538 -0.200 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4785 on 8669 degrees of freedom
## Multiple R-squared: 4.631e-06, Adjusted R-squared: -0.0001107
## F-statistic: 0.04014 on 1 and 8669 DF, p-value: 0.8412
4.1.13 Violent
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4066 -0.4066 -0.4066 0.5934 0.8054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19463 0.04012 4.851 1.25e-06 ***
## is_us1 0.21196 0.04047 5.237 1.67e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4898 on 8669 degrees of freedom
## Multiple R-squared: 0.003154, Adjusted R-squared: 0.003039
## F-statistic: 27.43 on 1 and 8669 DF, p-value: 1.669e-07
4.1.14 Action
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4703 -0.4703 -0.4703 0.5297 0.6778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32215 0.04085 7.886 3.5e-15 ***
## is_us1 0.14816 0.04121 3.596 0.000325 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4986 on 8669 degrees of freedom
## Multiple R-squared: 0.001489, Adjusted R-squared: 0.001374
## F-statistic: 12.93 on 1 and 8669 DF, p-value: 0.0003253
4.1.15 Emotional
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2081 -0.1907 -0.1907 -0.1907 0.8093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20805 0.03221 6.460 1.1e-10 ***
## is_us1 -0.01737 0.03249 -0.535 0.593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3931 on 8669 degrees of freedom
## Multiple R-squared: 3.298e-05, Adjusted R-squared: -8.237e-05
## F-statistic: 0.2859 on 1 and 8669 DF, p-value: 0.5929
4.1.16 Comedic
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4488 -0.4488 -0.4488 0.5512 0.6309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36913 0.04073 9.063 <2e-16 ***
## is_us1 0.07971 0.04109 1.940 0.0524 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4972 on 8669 degrees of freedom
## Multiple R-squared: 0.000434, Adjusted R-squared: 0.0003187
## F-statistic: 3.764 on 1 and 8669 DF, p-value: 0.0524
4.1.17 Experimental
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1745 -0.1632 -0.1632 -0.1632 0.8368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17450 0.03029 5.760 8.69e-09 ***
## is_us1 -0.01127 0.03056 -0.369 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3698 on 8669 degrees of freedom
## Multiple R-squared: 1.57e-05, Adjusted R-squared: -9.966e-05
## F-statistic: 0.1361 on 1 and 8669 DF, p-value: 0.7122
4.1.18 Strategy
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4488 -0.4488 -0.4488 0.5512 0.5571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.442953 0.040750 10.870 <2e-16 ***
## is_us1 0.005885 0.041105 0.143 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4974 on 8669 degrees of freedom
## Multiple R-squared: 2.365e-06, Adjusted R-squared: -0.000113
## F-statistic: 0.0205 on 1 and 8669 DF, p-value: 0.8862
4.1.19 Grinding
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5116 -0.5116 0.4884 0.4884 0.6510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34899 0.04092 8.528 < 2e-16 ***
## is_us1 0.16262 0.04128 3.940 8.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4995 on 8669 degrees of freedom
## Multiple R-squared: 0.001787, Adjusted R-squared: 0.001672
## F-statistic: 15.52 on 1 and 8669 DF, p-value: 8.227e-05
4.1.20 Anime
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17450 -0.07897 -0.07897 -0.07897 0.92103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17450 0.02228 7.831 5.40e-15 ***
## is_us1 -0.09552 0.02248 -4.250 2.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.272 on 8669 degrees of freedom
## Multiple R-squared: 0.002079, Adjusted R-squared: 0.001964
## F-statistic: 18.06 on 1 and 8669 DF, p-value: 2.16e-05
4.1.21 Hand drawn
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14094 -0.09939 -0.09939 -0.09939 0.90061
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14094 0.02459 5.732 1.02e-08 ***
## is_us1 -0.04155 0.02480 -1.675 0.0939 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3001 on 8669 degrees of freedom
## Multiple R-squared: 0.0003237, Adjusted R-squared: 0.0002083
## F-statistic: 2.807 on 1 and 8669 DF, p-value: 0.09391
4.1.22 Stylized
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5394 -0.5394 0.4606 0.4606 0.4832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.51678 0.04084 12.65 <2e-16 ***
## is_us1 0.02265 0.04120 0.55 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4985 on 8669 degrees of freedom
## Multiple R-squared: 3.487e-05, Adjusted R-squared: -8.048e-05
## F-statistic: 0.3023 on 1 and 8669 DF, p-value: 0.5825
4.1.23 Motivation: Action
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5419 -0.5419 0.4581 0.4581 0.6067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.39333 0.04067 9.671 < 2e-16 ***
## is_us1 0.14860 0.04103 3.622 0.000294 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4981 on 8649 degrees of freedom
## Multiple R-squared: 0.001514, Adjusted R-squared: 0.001399
## F-statistic: 13.12 on 1 and 8649 DF, p-value: 0.0002941
4.1.24 Motivation: Social
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4985 -0.4985 -0.3467 0.5015 0.6533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34667 0.04080 8.498 < 2e-16 ***
## is_us1 0.15186 0.04115 3.690 0.000226 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4996 on 8649 degrees of freedom
## Multiple R-squared: 0.001572, Adjusted R-squared: 0.001456
## F-statistic: 13.62 on 1 and 8649 DF, p-value: 0.0002256
4.1.25 Motivation: Mastery
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4607 -0.4607 -0.4607 0.5393 0.5800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42000 0.04070 10.32 <2e-16 ***
## is_us1 0.04065 0.04105 0.99 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4984 on 8649 degrees of freedom
## Multiple R-squared: 0.0001134, Adjusted R-squared: -2.252e-06
## F-statistic: 0.9805 on 1 and 8649 DF, p-value: 0.3221
4.1.26 Motivation: Achievement
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5292 -0.5292 0.4708 0.4708 0.5000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50000 0.04076 12.267 <2e-16 ***
## is_us1 0.02923 0.04112 0.711 0.477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4992 on 8649 degrees of freedom
## Multiple R-squared: 5.843e-05, Adjusted R-squared: -5.718e-05
## F-statistic: 0.5054 on 1 and 8649 DF, p-value: 0.4772
4.1.27 Motivation: Immersion
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4948 -0.4948 -0.3733 0.5052 0.6267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37333 0.04080 9.149 < 2e-16 ***
## is_us1 0.12143 0.04116 2.950 0.00319 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4998 on 8649 degrees of freedom
## Multiple R-squared: 0.001005, Adjusted R-squared: 0.0008897
## F-statistic: 8.703 on 1 and 8649 DF, p-value: 0.003186
4.1.28 Motivation: Creativity
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4567 -0.4567 -0.4567 0.5433 0.5733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42667 0.04067 10.491 <2e-16 ***
## is_us1 0.02999 0.04103 0.731 0.465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4981 on 8649 degrees of freedom
## Multiple R-squared: 6.175e-05, Adjusted R-squared: -5.386e-05
## F-statistic: 0.5341 on 1 and 8649 DF, p-value: 0.4649
4.2 To predict the probability of having a game label in a certain country, we use multilevel regression and post-stratification (MRP).
# Import libraries
source("read.R")
library(ggalt)
library(reshape2)
options(dplyr.summarise.inform = FALSE)In this subsection, we try to predict a probability of labeling a certain game with a particular Genome label by different countries.
# read csv
countryLevelDf <- read_csv("demographic.csv")## Rows: 7167 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Country, AgeGroup, Gender
## dbl (1): UserCount
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.countryLevelDf$UserCount <- as.numeric(countryLevelDf$UserCount)
# make the country name consistent with the individual survey dataframe
countryLevelDf <- countryLevelDf %>%
filter(Country %in% c("Argentina", "Brazil", "Chile", "Colombia", "Germany", "Greece", "India", "Japan", "Korea", "Mexico", "Nigeria", "Poland", "Saudi Arabia", "Singapore", "South Africa", "United States")) %>%
dplyr::rename(ageCut = AgeGroup, country = Country, gender = Gender) %>%
mutate(ageCut = case_when(ageCut == "18 - 24" ~ "18-24",
ageCut == "25 - 34" ~ "25-34",
ageCut == "35 - 44" ~ "35-44",
ageCut == "45 - 55" ~ "45-55",
ageCut == "> 55" ~ "55+"))
countryLevelDf$country[countryLevelDf$country == "United States"] <- "US"
countryLevelDf$country[countryLevelDf$country == "Saudi Arabia"] <- "Saudi.Arabia"
countryLevelDf$country[countryLevelDf$country == "South Africa"] <- "South.Africa"
countryLevelDf <- countryLevelDf %>% mutate(region = case_when(country %in% c("Japan", "Korea") ~ "Confucian",
country %in% c("Singapore", "India") ~ "West.South.Asia",
country %in% c("Argentina", "Chile", "Colombia", "Mexico", "Brazil") ~ "Latin.America",
country %in% c("Germany") ~ "Protestant.Europe",
country %in% c("South.Africa", "Nigeria", "Saudi.Arabia") ~ "Islamic",
country %in% c("Poland") ~ "Catholic.Europe",
country %in% c("Greece") ~ "Orthodox.Europe",
country %in% c("US") ~ "US"))
countryLevelDf$country <- factor(countryLevelDf$country)
countryLevelDf$ageCut <- factor(countryLevelDf$ageCut, c("18-24","25-34", "35-44", "45-55", "55+"))
countryLevelDf$gender <- factor(countryLevelDf$gender)
countryLevelDf$region = factor(countryLevelDf$region)
# Calculate the percentage of age+gender group by country
countryLevelDf <- countryLevelDf %>% group_by(country) %>%
mutate(UserCount.per = UserCount/sum(UserCount, na.rm=TRUE))
countryLevelDf <- countryLevelDf %>% filter(ageCut != "" & ageCut != "Unknown" & !is.na(ageCut)) %>%
filter(gender != "" & gender != "Unknown" & !is.na(gender)) %>%
mutate(gender = ifelse(gender == "Male", "Male", "Female or Nonbinary"))4.3 Exploratory Data Analysis
We examine the difference between our sample of ~5000 participants and the true MS population.
source("read.R")
df <- df %>% mutate(gender = ifelse(gender == "Male", "Male", "Female or Nonbinary"))
age_sample <- df %>%
dplyr::mutate(age = factor(ageCut, ordered = FALSE)) %>%
dplyr::group_by(age) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(Sample = n/sum(n))
age_post <- countryLevelDf %>%
dplyr::mutate(age = factor(ageCut, ordered = FALSE)) %>%
dplyr::group_by(age) %>%
dplyr::summarise(n_post = sum(UserCount)) %>%
dplyr::mutate(Population = n_post/sum(n_post))
age <- dplyr::inner_join(age_sample, age_post, by = "age") %>% select(age, Sample, Population)
age_plot <- ggplot() +
ylab("") + xlab("Proportion") + theme_bw() + coord_flip() +
geom_dumbbell(data = age, aes(y = age, x = Sample, xend = Population)) +
geom_point(data = melt(age, id = "age"), aes(y = age, x = value, color = variable), size = 2) +
scale_x_continuous(limits = c(0, 0.5), breaks = c(0, .1, .2, .3, .4, .5)) +
ggtitle("Age")
age_plot
# Gender because the poststratification dataset does not have nonbinary
male_sample <- df %>%
dplyr::mutate(gender = ifelse(gender != "Male", "Female or Nonbinary", "Male")) %>%
dplyr::group_by(gender) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(Sample = n/sum(n))
male_post <- countryLevelDf %>%
dplyr::group_by(gender) %>%
dplyr::summarise(n_post = sum(UserCount)) %>%
dplyr::mutate(Population = n_post/sum(n_post))
male <- dplyr::inner_join(male_sample, male_post, by = "gender") %>% select(gender, Sample, Population)
male_plot <- ggplot() +
ylab("") + xlab("") + theme_bw() + coord_flip() +
geom_dumbbell(data = male, aes(y = gender, x = Sample, xend = Population)) +
geom_point(data = melt(male, id = "gender"), aes(y = gender, x = value, color = variable), size = 2) +
scale_x_continuous(limits = c(0, 1), breaks = c(0, .2, .4, .6, .8, 1.0)) + ggtitle("Gender")
male_plot
4.3.1 Poststratification for country
- Here we further show that for the US, it is way higher likely that these games such as PUBG, Elden Ring, GTA, Call of Duty to be violent.
4.3.1.1 TODO: Add the hardcode percentage before merging the table
library(data.table)
poststratify <- function(game, label) {
temp <- df %>% filter(label == {{ label }} & game == {{ game }}) %>%
filter(accessibility %in% c("Yes", "No")) %>%
mutate(gender = ifelse(gender == "Male", "Male", "Female or Nonbinary")) %>%
na.omit()
mod <- glm(answer ~ hardcore + accessibility + country + gender + ageCut, data = temp, family = binomial(link="logit"))
post <- expand.grid(accessibility=unique(temp$accessibility),
country=unique(temp$country),
gender=unique(temp$gender),
ageCut=unique(temp$ageCut),
hardcore=unique(temp$hardcore))
post <- post %>%
mutate(gender = ifelse(gender != "Male", "Female", "Male")) %>%
left_join(countryLevelDf, by=c("country", "gender", "ageCut")) %>%
mutate(gender = ifelse(gender != "Male", "Female or Nonbinary", "Male"))
post <- post %>%
mutate(UserCount.per = ifelse(accessibility == "No", UserCount.per * 0.1, UserCount.per * 0.9)) # heuristic about accessibility
post$prediction <- predict(mod, newdata=post, type="response", allow.new.levels=TRUE)
post$weight.pred <- post$prediction*post$UserCount.per
results <- data.table(post)[ , .(final.est = sum(weight.pred, na.rm=TRUE)), by = .(country)]
return(results)
}
labelLst = c("pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space", "heroic", "real.world", "violent",
"action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
gameLst <- unique(df$game)
predicted.df <- data.frame(country=c(), final.est=c(), label=c(), game = c())
# Calculate the poststratified probability
for(g in gameLst) {
for(l in labelLst) {
label.df <- poststratify(g, l)
label.df$label <- l
label.df$game <- g
predicted.df <- rbind(predicted.df, label.df)
}
}## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# View the countries with a probability > 0.75
predicted.df %>% filter(final.est > 0.5 & label == "violent")## country final.est label game
## 1: US 0.7451460 violent PUBG
## 2: Greece 0.5973012 violent PUBG
## 3: Japan 0.5699150 violent PUBG
## 4: Mexico 0.5149857 violent PUBG
## 5: Poland 0.5240003 violent PUBG
## 6: US 0.8926281 violent Grand Theft Auto V
## 7: Singapore 0.6421735 violent Grand Theft Auto V
## 8: South.Africa 0.5146014 violent Grand Theft Auto V
## 9: Brazil 0.5838548 violent Grand Theft Auto V
## 10: Chile 0.5220274 violent Grand Theft Auto V
## 11: Colombia 0.5268495 violent Grand Theft Auto V
## 12: Germany 0.7017908 violent Grand Theft Auto V
## 13: Greece 0.7147602 violent Grand Theft Auto V
## 14: Japan 0.5895735 violent Grand Theft Auto V
## 15: Korea 0.6300997 violent Grand Theft Auto V
## 16: Mexico 0.6231587 violent Grand Theft Auto V
## 17: Poland 0.5453907 violent Grand Theft Auto V
## 18: US 0.8339568 violent Elden Ring
## 19: Singapore 0.5073604 violent Elden Ring
## 20: Germany 0.5004755 violent Elden Ring
## 21: Greece 0.5778392 violent Elden Ring
## 22: Poland 0.5420260 violent Elden Ring
## 23: US 0.6905598 violent Call of Duty: Vanguard
## 24: Germany 0.5635139 violent Call of Duty: Vanguard
## 25: Greece 0.5231360 violent Call of Duty: Vanguard
## country final.est label game4.3.2 Alternative approach using rstanarm
https://bookdown.org/jl5522/MRP-case-studies/introduction-to-mrp.html
# res <- df %>% filter(label == "made.for.kids") %>%
# filter(!label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art")) %>%
# filter(!is.na(country) & !is.na(gender) & !is.na(Language) & !is.na(ageCut)) %>%
# mutate(male = ifelse(gender == "Male", 1, 0)) %>%
# mutate(male = as.factor(male)) %>%
# select(Response.ID, country, male, ageCut, region, answer)
# library(rstanarm)
# fit <- stan_glmer(answer ~ (1|country) + (1|ageCut) + male + region,
# family = binomial(link = "logit"),
# data = res,
# prior = normal(0, 1, autoscale = TRUE),
# prior_covariance = decov(scale = 0.50),
# adapt_delta = 0.99,
# refresh = 0,
# seed = 1010)4.4 Poststratification for cultural correlation analysis (comparing label correlation and Hofstede’s correlation)
library(lme4)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod carBelow, I will show a Multi-level regression and poststratification (MRP) on one label (violent) and one game (Grand Theft Auto V) as an example. I will use the same approach to generate a prediction for all label-game pair for different countries. In the end, I will use the predicted value as element in the label vector to compare with Hofstede’s cultural dimensions.
4.4.1 MPR Example
4.4.1.1 Prep the groundtruth dataset
# First we need to prep the groundtruth dataset when merging the groundtruth countryLevelDf for post-stratification
# Make the dataset from a long format to a wide format
wide.df <- countryLevelDf %>% dplyr::select(country, gender, ageCut, UserCount) %>%
pivot_wider(names_from = country, values_from = UserCount)
wide.df <- wide.df[with(wide.df, order(gender, ageCut)),]
wide.df <- wide.df %>% dplyr::select(-ageCut, -gender)
# Ascertain the order of the random variables
country.order <- colnames(wide.df)
age.order <- unique(df$ageCut[!is.na(df$ageCut)])
gender.order <- unique(df$gender[!is.na(df$gender)])# Filter a smaller dataset and generate the model
violent <- df %>% filter(label == "violent" & game == "Grand Theft Auto V")
mod <- glmer(answer ~ 1 + (1|gender) + (1|ageCut) + (1|country), data=violent, family = binomial("probit"))4.4.1.2 Generate Prediction for ideal types (5 ageCuts * 2 genders * 16 countries)
# We have 10 individual level (5 ageCut * 2 gender) living in 16 countries. There are 10*16 specific ideal types.
# Reference of the implementation: https://github.com/lleemann/MrP_chapter/blob/master/MrP_Illsutration.pdf
re.gender.coef <- ranef(mod)$gender
re.age.coef <- ranef(mod)$ageCut
re.country.coef <- ranef(mod)$country
rownames(re.gender.coef) <- gender.order
rownames(re.age.coef) <- age.order
rownames(re.country.coef) <- country.order
re.gender <- re.gender.coef[[1]]
re.ageCut <- re.age.coef[[1]]
re.country <- re.country.coef[[1]]
gender.re <- rep(re.gender, 5)
age.re <- rep(kronecker(re.ageCut, c(1)), 1)
ind.re <- rowSums(cbind(gender.re, age.re))
ind.re <- ind.re + fixef(mod)
y.lat <- rep(NA, 16*10)
for(i in 1:16) {
a <- ((i-1)*10)+1
b <- a + 9
y.lat[a:b] <- ind.re + re.country[i]
}
# Here we use pnorm to transform the scores in y.lat1 to predicted probability
# In other words, what we have doe was on the latent variable, but we are really interested in the predicted probability
p1 <- pnorm(y.lat)
# p1 is the vector for 10 individual levels in 16 countries
# It's essentially the random effect coefficients
length(p1) ## [1] 1604.4.1.3 Post-stratify by ideal types
# Next, we can post-stratify and make predictions
a <- c(data.matrix(wide.df)) # get the number of groudtruth user count in the 160 categories above
# Post-stratified number for a label for a game (in this case "violent", GTA V)
# This number shows the prediction regardless of countries: pretty consistent hah
sum(p1*a)/sum(a)## [1] 0.7234758# Now we post-stratify the individual levels (5 ageCut * 2 gender) for each country
country.pred <- rep(NA, 16)
for(i in 1:16) {
# a1, a2 are indices
a1 <- ((i-1)*10) + 1
a2 <- a1 + 9
p1 <- pnorm(y.lat[a1:a2])
a <- wide.df[,i] # We don't need to worry about the order because we have considered the order when we generate y.lat1
country.pred[i] <- sum(p1*a) / sum(a)
}
# Chile is really not thinking that GTA is a violent game
plot.df <- data.frame(country = factor(colnames(wide.df), levels=colnames(wide.df)), predicted=country.pred)
ggplot(aes(x=reorder(country, predicted), y=predicted), data=plot.df) +
geom_point()
4.4.2 MRP for all label-game pairs for each country
The goal here is to create a dataframe with columns: label, game, country, glm_pred, mrp_adjusted
# Let's consider 0-1 vector first
labels <- c("pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space", "heroic", "real.world", "violent", "action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn", "stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
games <- unique(df$game)
# This function repeats what we have done in the example above
getMRPPrediction <- function(mod) {
re.gender.coef <- ranef(mod)$gender
re.age.coef <- ranef(mod)$ageCut
re.country.coef <- ranef(mod)$country
rownames(re.gender.coef) <- gender.order
rownames(re.age.coef) <- age.order
rownames(re.country.coef) <- country.order
re.gender <- re.gender.coef[[1]]
re.ageCut <- re.age.coef[[1]]
re.country <- re.country.coef[[1]]
gender.re <- rep(re.gender, 5)
age.re <- rep(kronecker(re.ageCut, c(1)), 1)
ind.re <- rowSums(cbind(gender.re, age.re))
ind.re <- ind.re + fixef(mod)
y.lat <- rep(NA, 16*10)
for(i in 1:16) {
a <- ((i-1)*10)+1
b <- a + 9
y.lat[a:b] <- ind.re + re.country[i]
}
p1 <- pnorm(y.lat)
# post-stratification prediction
country.pred <- rep(NA, 16)
for(i in 1:16) {
a1 <- ((i-1)*10) + 1
a2 <- a1 + 9
p1 <- pnorm(y.lat[a1:a2])
a <- wide.df[,i]
country.pred[i] <- sum(p1*a) / sum(a)
}
# append MRP prediction
output.df <- data.frame(country = factor(colnames(wide.df), levels=country.order), mrp_pred=country.pred)
return(output.df)
}
getGLMPrediction = function(mod) {
pred <- expand.grid(country=country.order)
pred$glm_pred <- predict(mod, newdata=pred, type="response", allow.new.levels=TRUE)
return(pred)
}
# Here we create the final.df which is the post-stratified prediction for each country
final.df <- data.frame(label=c(), country=c(), game=c(), glm_pred=c(), mrp_adjusted=c())
for(label in labels){
for(game in games){
label.df <- df %>% filter(label == {{ label }} & game == {{ game }})
mod.glm <- glm(answer ~ country, data=label.df, family = binomial(link="logit"))
mod.mrp <- glmer(answer ~ 1 + (1|gender) + (1|ageCut) + (1|country), data=label.df, family = binomial("probit"))
glm.pred <- getGLMPrediction(mod.glm)
mrp.pred <- getMRPPrediction(mod.mrp)
mrp.pred <- mrp.pred %>% left_join(glm.pred, by="country")
mrp.pred$game <- game
mrp.pred$label <- label
final.df <- rbind(mrp.pred, final.df)
}
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## boundary (singular) fit: see ?isSingularhead(final.df)## country mrp_pred glm_pred game label
## 1 South.Africa 0.8286466 0.7692308 The Sims 3 creativity
## 2 Colombia 0.8376679 0.7500000 The Sims 3 creativity
## 3 Greece 0.8194868 0.8666667 The Sims 3 creativity
## 4 Korea 0.8249671 0.9999999 The Sims 3 creativity
## 5 India 0.8325641 0.9999999 The Sims 3 creativity
## 6 Japan 0.8127111 0.6250000 The Sims 3 creativity## We need to filter out country with very low survey responses
filter.df <- df %>% group_by(label, country, game) %>%
filter(n() >= 5) %>%
count()
final.df <- final.df %>%
left_join(filter.df, by=c("country", "game", "label")) %>%
mutate(mrp_pred=ifelse(n < 5, NA, final.df$mrp_pred)) %>%
mutate(glm_pred=ifelse(n < 5, NA, final.df$glm_pred))head(final.df)## country mrp_pred glm_pred game label n
## 1 South.Africa 0.8286466 0.7692308 The Sims 3 creativity 13
## 2 Colombia 0.8376679 0.7500000 The Sims 3 creativity 12
## 3 Greece 0.8194868 0.8666667 The Sims 3 creativity 15
## 4 Korea 0.8249671 0.9999999 The Sims 3 creativity 6
## 5 India NA NA The Sims 3 creativity NA
## 6 Japan 0.8127111 0.6250000 The Sims 3 creativity 84.4.3 Heatmap
# The post-stratified prediction
final.df %>% ggplot(aes(x=country, y=label, fill=mrp_pred)) +
geom_tile() +
facet_wrap(~ game, scales="free", ncol=3)
4.4.4 Comparing post-stratified results with the Hofstede’s Dimensions
Now we have the post-stratified results, we should compare with Hoftstede Cultural Dimensions.
library(reshape)
library(coop)
# helper
# https://stats.stackexchange.com/questions/31565/compute-a-cosine-dissimilarity-matrix-in-r
# cos.sim <- function(ix) {
# A = X[ix[1],]
# B = X[ix[2],]
# return( sum(A*B)/sqrt(sum(A^2)*sum(B^2)) )
# }
euclidean <- function(a, b) sqrt(sum((a - b)^2))# We create a dataframe where we calculate the cosine similarity between two countries
hofstedeDf <- read.csv("hoftstede.csv")
hofstedeDf <- hofstedeDf %>% select(-X) %>%
mutate(power.distance = power.distance/100,
individualism = individualism/100,
masculinity = masculinity/100,
uncertainty = uncertainty/100,
long.term.orientation = long.term.orientation/100,
indulgence = indulgence/100) # scale the hofstede score to 0 - 1
# Create a matrix with cosine similarity between countries
hofstedeMatrix = as.matrix(hofstedeDf[,2:7])
m <- dist(hofstedeMatrix, method = "euclidean", diag = TRUE, upper = TRUE, p = 2) # switch to Euclidean distance
m <- as.matrix(m, nrow=16, ncol=16)
rownames(m) <- hofstedeDf$country
colnames(m) <- hofstedeDf$country
cultureDf <- data.frame(countryA=rownames(m)[row(m)], countryB=colnames(m)[col(m)], culture.euclidean=c(m))
head(cultureDf)## countryA countryB culture.euclidean
## 1 India India 0.0000000
## 2 Singapore India 0.5605355
## 3 US India 0.8330066
## 4 Nigeria India 0.8075890
## 5 South.Africa India 0.6466065
## 6 Argentina India 0.7785242# This function takes a matrix where columns are countries
# This function returns the correlation between countries
# countryA, countryB, label.corr
calcCosDf <- function(full, label) {
full
label
# mx <- cor(full, use = "pairwise.complete.obs")
mx <- as.matrix(full)
m <- cosine(mx)
retDf <- data.frame(countryA=rownames(m)[row(m)], countryB=colnames(m)[col(m)], label.cos.sim=c(m))
retDf$label <- label
return(retDf)
}
# Euclidean distance calculation
calEuclidean <- function(full, label) {
full
label
# mx <- cor(full, use = "pairwise.complete.obs")
mx <- t(as.matrix(full))
m <- dist(mx, method = "euclidean", diag = TRUE, upper = TRUE, p = 2)
m <- as.matrix(m, nrow=16, ncol=16)
retDf <- data.frame(countryA=rownames(m)[row(m)], countryB=colnames(m)[col(m)], label.euclidean=c(m))
retDf$label <- label
return(retDf)
}# This is calculating the distance between game vectors (each dot is represented by game)
# labels currently using the binary indicator variable
countries <- unique(df$country)
gameOrder <- unique(df$game[!is.na(df$game)])
output <- data.frame(countryA=c(), countryB=c(), label.cos.sim=c(), label=c())
for(l in labels) {
labelSpace <- data.frame(matrix(ncol = 0, nrow=11))
for(country in countries) {
dfA <- final.df %>% filter(country == !!country & label == !!l)
dfA <- dfA %>% arrange(factor(game, levels=gameOrder))
v <- as.vector(dfA$mrp_pred)
labelSpace <- cbind(labelSpace, as.data.frame(v))
names(labelSpace)[names(labelSpace) == "v"] <- country
}
output <- rbind(calEuclidean(labelSpace, l), output)
}# This is calculating the distance between label vectors (each dot is represented by labels)
# labels currently using the binary indicator variable
countries <- unique(df$country)
gameOrder <- unique(df$game[!is.na(df$game)])
labelOrder <- c("pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space", "heroic", "real.world", "violent", "action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn", "stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
output.game <- data.frame(countryA=c(), countryB=c(), label.cos.sim=c(), label=c())
for(game in gameOrder) {
gameSpace <- data.frame(matrix(ncol=0, nrow=24))
for(country in countries) {
dfB <- final.df %>% filter(country == !!country & game == !!game)
dfB <- dfB %>% arrange(factor(label, levels=labelOrder))
v <- dfB$mrp_pred
gameSpace <- cbind(gameSpace, as.data.frame(v))
names(gameSpace)[names(gameSpace) == "v"] <- country
}
output.game <- rbind(calEuclidean(gameSpace, game))
}
output.game## countryA countryB label.euclidean label
## 1 US US 0.00000000 The Sims 3
## 2 India US NA The Sims 3
## 3 Singapore US NA The Sims 3
## 4 South.Africa US 0.22505351 The Sims 3
## 5 Nigeria US 0.12879605 The Sims 3
## 6 Argentina US 0.13634817 The Sims 3
## 7 Brazil US 0.24325714 The Sims 3
## 8 Chile US 0.16535898 The Sims 3
## 9 Colombia US 0.34438118 The Sims 3
## 10 Germany US 0.14137107 The Sims 3
## 11 Greece US 0.22707679 The Sims 3
## 12 Japan US 0.30844855 The Sims 3
## 13 Korea US 0.16904016 The Sims 3
## 14 Mexico US 0.22531273 The Sims 3
## 15 Poland US 0.21986125 The Sims 3
## 16 Saudi.Arabia US NA The Sims 3
## 17 US India NA The Sims 3
## 18 India India 0.00000000 The Sims 3
## 19 Singapore India NA The Sims 3
## 20 South.Africa India NA The Sims 3
## 21 Nigeria India NA The Sims 3
## 22 Argentina India NA The Sims 3
## 23 Brazil India NA The Sims 3
## 24 Chile India NA The Sims 3
## 25 Colombia India NA The Sims 3
## 26 Germany India NA The Sims 3
## 27 Greece India NA The Sims 3
## 28 Japan India NA The Sims 3
## 29 Korea India NA The Sims 3
## 30 Mexico India NA The Sims 3
## 31 Poland India NA The Sims 3
## 32 Saudi.Arabia India NA The Sims 3
## 33 US Singapore NA The Sims 3
## 34 India Singapore NA The Sims 3
## 35 Singapore Singapore 0.00000000 The Sims 3
## 36 South.Africa Singapore NA The Sims 3
## 37 Nigeria Singapore NA The Sims 3
## 38 Argentina Singapore NA The Sims 3
## 39 Brazil Singapore NA The Sims 3
## 40 Chile Singapore NA The Sims 3
## 41 Colombia Singapore NA The Sims 3
## 42 Germany Singapore NA The Sims 3
## 43 Greece Singapore NA The Sims 3
## 44 Japan Singapore NA The Sims 3
## 45 Korea Singapore NA The Sims 3
## 46 Mexico Singapore NA The Sims 3
## 47 Poland Singapore NA The Sims 3
## 48 Saudi.Arabia Singapore NA The Sims 3
## 49 US South.Africa 0.22505351 The Sims 3
## 50 India South.Africa NA The Sims 3
## 51 Singapore South.Africa NA The Sims 3
## 52 South.Africa South.Africa 0.00000000 The Sims 3
## 53 Nigeria South.Africa 0.28953504 The Sims 3
## 54 Argentina South.Africa 0.25067356 The Sims 3
## 55 Brazil South.Africa 0.28624684 The Sims 3
## 56 Chile South.Africa 0.28827462 The Sims 3
## 57 Colombia South.Africa 0.45228434 The Sims 3
## 58 Germany South.Africa 0.27667318 The Sims 3
## 59 Greece South.Africa 0.34348600 The Sims 3
## 60 Japan South.Africa 0.40511123 The Sims 3
## 61 Korea South.Africa 0.33625561 The Sims 3
## 62 Mexico South.Africa 0.35766063 The Sims 3
## 63 Poland South.Africa 0.31539808 The Sims 3
## 64 Saudi.Arabia South.Africa NA The Sims 3
## 65 US Nigeria 0.12879605 The Sims 3
## 66 India Nigeria NA The Sims 3
## 67 Singapore Nigeria NA The Sims 3
## 68 South.Africa Nigeria 0.28953504 The Sims 3
## 69 Nigeria Nigeria 0.00000000 The Sims 3
## 70 Argentina Nigeria 0.09978512 The Sims 3
## 71 Brazil Nigeria 0.23442456 The Sims 3
## 72 Chile Nigeria 0.12017789 The Sims 3
## 73 Colombia Nigeria 0.23080710 The Sims 3
## 74 Germany Nigeria 0.14342275 The Sims 3
## 75 Greece Nigeria 0.15614066 The Sims 3
## 76 Japan Nigeria 0.35181560 The Sims 3
## 77 Korea Nigeria 0.12211008 The Sims 3
## 78 Mexico Nigeria 0.18390054 The Sims 3
## 79 Poland Nigeria 0.14279187 The Sims 3
## 80 Saudi.Arabia Nigeria NA The Sims 3
## 81 US Argentina 0.13634817 The Sims 3
## 82 India Argentina NA The Sims 3
## 83 Singapore Argentina NA The Sims 3
## 84 South.Africa Argentina 0.25067356 The Sims 3
## 85 Nigeria Argentina 0.09978512 The Sims 3
## 86 Argentina Argentina 0.00000000 The Sims 3
## 87 Brazil Argentina 0.20612227 The Sims 3
## 88 Chile Argentina 0.10455169 The Sims 3
## 89 Colombia Argentina 0.27517836 The Sims 3
## 90 Germany Argentina 0.15761274 The Sims 3
## 91 Greece Argentina 0.17507426 The Sims 3
## 92 Japan Argentina 0.31166698 The Sims 3
## 93 Korea Argentina 0.20616144 The Sims 3
## 94 Mexico Argentina 0.18548770 The Sims 3
## 95 Poland Argentina 0.15434329 The Sims 3
## 96 Saudi.Arabia Argentina NA The Sims 3
## 97 US Brazil 0.24325714 The Sims 3
## 98 India Brazil NA The Sims 3
## 99 Singapore Brazil NA The Sims 3
## 100 South.Africa Brazil 0.28624684 The Sims 3
## 101 Nigeria Brazil 0.23442456 The Sims 3
## 102 Argentina Brazil 0.20612227 The Sims 3
## 103 Brazil Brazil 0.00000000 The Sims 3
## 104 Chile Brazil 0.20585476 The Sims 3
## 105 Colombia Brazil 0.38811534 The Sims 3
## 106 Germany Brazil 0.20194577 The Sims 3
## 107 Greece Brazil 0.24443416 The Sims 3
## 108 Japan Brazil 0.39765705 The Sims 3
## 109 Korea Brazil 0.29212015 The Sims 3
## 110 Mexico Brazil 0.35400213 The Sims 3
## 111 Poland Brazil 0.21170219 The Sims 3
## 112 Saudi.Arabia Brazil NA The Sims 3
## 113 US Chile 0.16535898 The Sims 3
## 114 India Chile NA The Sims 3
## 115 Singapore Chile NA The Sims 3
## 116 South.Africa Chile 0.28827462 The Sims 3
## 117 Nigeria Chile 0.12017789 The Sims 3
## 118 Argentina Chile 0.10455169 The Sims 3
## 119 Brazil Chile 0.20585476 The Sims 3
## 120 Chile Chile 0.00000000 The Sims 3
## 121 Colombia Chile 0.27053315 The Sims 3
## 122 Germany Chile 0.18224458 The Sims 3
## 123 Greece Chile 0.16543564 The Sims 3
## 124 Japan Chile 0.37104157 The Sims 3
## 125 Korea Chile 0.21596757 The Sims 3
## 126 Mexico Chile 0.24034773 The Sims 3
## 127 Poland Chile 0.16054306 The Sims 3
## 128 Saudi.Arabia Chile NA The Sims 3
## 129 US Colombia 0.34438118 The Sims 3
## 130 India Colombia NA The Sims 3
## 131 Singapore Colombia NA The Sims 3
## 132 South.Africa Colombia 0.45228434 The Sims 3
## 133 Nigeria Colombia 0.23080710 The Sims 3
## 134 Argentina Colombia 0.27517836 The Sims 3
## 135 Brazil Colombia 0.38811534 The Sims 3
## 136 Chile Colombia 0.27053315 The Sims 3
## 137 Colombia Colombia 0.00000000 The Sims 3
## 138 Germany Colombia 0.34420513 The Sims 3
## 139 Greece Colombia 0.29060349 The Sims 3
## 140 Japan Colombia 0.51195050 The Sims 3
## 141 Korea Colombia 0.27418658 The Sims 3
## 142 Mexico Colombia 0.26442587 The Sims 3
## 143 Poland Colombia 0.28583090 The Sims 3
## 144 Saudi.Arabia Colombia NA The Sims 3
## 145 US Germany 0.14137107 The Sims 3
## 146 India Germany NA The Sims 3
## 147 Singapore Germany NA The Sims 3
## 148 South.Africa Germany 0.27667318 The Sims 3
## 149 Nigeria Germany 0.14342275 The Sims 3
## 150 Argentina Germany 0.15761274 The Sims 3
## 151 Brazil Germany 0.20194577 The Sims 3
## 152 Chile Germany 0.18224458 The Sims 3
## 153 Colombia Germany 0.34420513 The Sims 3
## 154 Germany Germany 0.00000000 The Sims 3
## 155 Greece Germany 0.22596416 The Sims 3
## 156 Japan Germany 0.29361145 The Sims 3
## 157 Korea Germany 0.17397507 The Sims 3
## 158 Mexico Germany 0.28334712 The Sims 3
## 159 Poland Germany 0.17409191 The Sims 3
## 160 Saudi.Arabia Germany NA The Sims 3
## 161 US Greece 0.22707679 The Sims 3
## 162 India Greece NA The Sims 3
## 163 Singapore Greece NA The Sims 3
## 164 South.Africa Greece 0.34348600 The Sims 3
## 165 Nigeria Greece 0.15614066 The Sims 3
## 166 Argentina Greece 0.17507426 The Sims 3
## 167 Brazil Greece 0.24443416 The Sims 3
## 168 Chile Greece 0.16543564 The Sims 3
## 169 Colombia Greece 0.29060349 The Sims 3
## 170 Germany Greece 0.22596416 The Sims 3
## 171 Greece Greece 0.00000000 The Sims 3
## 172 Japan Greece 0.40630023 The Sims 3
## 173 Korea Greece 0.20192862 The Sims 3
## 174 Mexico Greece 0.25495132 The Sims 3
## 175 Poland Greece 0.12423208 The Sims 3
## 176 Saudi.Arabia Greece NA The Sims 3
## 177 US Japan 0.30844855 The Sims 3
## 178 India Japan NA The Sims 3
## 179 Singapore Japan NA The Sims 3
## 180 South.Africa Japan 0.40511123 The Sims 3
## 181 Nigeria Japan 0.35181560 The Sims 3
## 182 Argentina Japan 0.31166698 The Sims 3
## 183 Brazil Japan 0.39765705 The Sims 3
## 184 Chile Japan 0.37104157 The Sims 3
## 185 Colombia Japan 0.51195050 The Sims 3
## 186 Germany Japan 0.29361145 The Sims 3
## 187 Greece Japan 0.40630023 The Sims 3
## 188 Japan Japan 0.00000000 The Sims 3
## 189 Korea Japan 0.37879009 The Sims 3
## 190 Mexico Japan 0.37186489 The Sims 3
## 191 Poland Japan 0.39004053 The Sims 3
## 192 Saudi.Arabia Japan NA The Sims 3
## 193 US Korea 0.16904016 The Sims 3
## 194 India Korea NA The Sims 3
## 195 Singapore Korea NA The Sims 3
## 196 South.Africa Korea 0.33625561 The Sims 3
## 197 Nigeria Korea 0.12211008 The Sims 3
## 198 Argentina Korea 0.20616144 The Sims 3
## 199 Brazil Korea 0.29212015 The Sims 3
## 200 Chile Korea 0.21596757 The Sims 3
## 201 Colombia Korea 0.27418658 The Sims 3
## 202 Germany Korea 0.17397507 The Sims 3
## 203 Greece Korea 0.20192862 The Sims 3
## 204 Japan Korea 0.37879009 The Sims 3
## 205 Korea Korea 0.00000000 The Sims 3
## 206 Mexico Korea 0.24795509 The Sims 3
## 207 Poland Korea 0.20564284 The Sims 3
## 208 Saudi.Arabia Korea NA The Sims 3
## 209 US Mexico 0.22531273 The Sims 3
## 210 India Mexico NA The Sims 3
## 211 Singapore Mexico NA The Sims 3
## 212 South.Africa Mexico 0.35766063 The Sims 3
## 213 Nigeria Mexico 0.18390054 The Sims 3
## 214 Argentina Mexico 0.18548770 The Sims 3
## 215 Brazil Mexico 0.35400213 The Sims 3
## 216 Chile Mexico 0.24034773 The Sims 3
## 217 Colombia Mexico 0.26442587 The Sims 3
## 218 Germany Mexico 0.28334712 The Sims 3
## 219 Greece Mexico 0.25495132 The Sims 3
## 220 Japan Mexico 0.37186489 The Sims 3
## 221 Korea Mexico 0.24795509 The Sims 3
## 222 Mexico Mexico 0.00000000 The Sims 3
## 223 Poland Mexico 0.26123688 The Sims 3
## 224 Saudi.Arabia Mexico NA The Sims 3
## 225 US Poland 0.21986125 The Sims 3
## 226 India Poland NA The Sims 3
## 227 Singapore Poland NA The Sims 3
## 228 South.Africa Poland 0.31539808 The Sims 3
## 229 Nigeria Poland 0.14279187 The Sims 3
## 230 Argentina Poland 0.15434329 The Sims 3
## 231 Brazil Poland 0.21170219 The Sims 3
## 232 Chile Poland 0.16054306 The Sims 3
## 233 Colombia Poland 0.28583090 The Sims 3
## 234 Germany Poland 0.17409191 The Sims 3
## 235 Greece Poland 0.12423208 The Sims 3
## 236 Japan Poland 0.39004053 The Sims 3
## 237 Korea Poland 0.20564284 The Sims 3
## 238 Mexico Poland 0.26123688 The Sims 3
## 239 Poland Poland 0.00000000 The Sims 3
## 240 Saudi.Arabia Poland NA The Sims 3
## 241 US Saudi.Arabia NA The Sims 3
## 242 India Saudi.Arabia NA The Sims 3
## 243 Singapore Saudi.Arabia NA The Sims 3
## 244 South.Africa Saudi.Arabia NA The Sims 3
## 245 Nigeria Saudi.Arabia NA The Sims 3
## 246 Argentina Saudi.Arabia NA The Sims 3
## 247 Brazil Saudi.Arabia NA The Sims 3
## 248 Chile Saudi.Arabia NA The Sims 3
## 249 Colombia Saudi.Arabia NA The Sims 3
## 250 Germany Saudi.Arabia NA The Sims 3
## 251 Greece Saudi.Arabia NA The Sims 3
## 252 Japan Saudi.Arabia NA The Sims 3
## 253 Korea Saudi.Arabia NA The Sims 3
## 254 Mexico Saudi.Arabia NA The Sims 3
## 255 Poland Saudi.Arabia NA The Sims 3
## 256 Saudi.Arabia Saudi.Arabia 0.00000000 The Sims 3# Plotting the correlation
combined <- cultureDf %>% left_join(output, by=c("countryA", "countryB")) %>%
filter(countryA != countryB) %>%
filter(!duplicated(paste0(pmax(countryA, countryB), pmin(countryA, countryB), label)))
ggscatter(combined, x = "culture.euclidean", y = "label.euclidean",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "Hofstede Cosine Similarity", ylab = "Label Cosine Similarity")## `geom_smooth()` using formula 'y ~ x'
# Plotting the correlation
combined <- cultureDf %>% left_join(output.game, by=c("countryA", "countryB")) %>%
filter(countryA != countryB) %>%
filter(!duplicated(paste0(pmax(countryA, countryB), pmin(countryA, countryB), label)))
ggscatter(combined, x = "culture.euclidean", y = "label.euclidean",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "Hofstede Euclidean Distance", ylab = "Label Euclidean Distance")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 42 rows containing non-finite values (stat_smooth).## Warning: Removed 42 rows containing non-finite values (stat_cor).## Warning: Removed 42 rows containing missing values (geom_point).
X-axis is the Euclidean distance of the Hofstede’s dimension between country pairs. Y-axis is the Euclidean distance of two 11-dimensional vectors between two countries. The 24-dimensional vectors refer to 0-1 binary labels (e.g., [pacifist, violent, …]) for a specific game.
5 Figure 3: If label outcome differences are found across countries, is there a data-backed explanation relating to culture?
To answer this question, we use the Hofstede’s theory of cultural dimensions. This question answers whether culturally “closer” countries have more similar survey results.
- Define ‘culture’ using Hofstede’s cultural dimension map and calculate cultural similarity scores between pairs of countries
- Calculate correlations between sampled survey responses for individuals across pairs of countries
library(tidyverse)
library(tidytext)
library(data.table)
library("ggpubr")
source("read.R")
theme_set(
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8))
)
labels <- unique(df$label)# 1. Find the pairwise correlation between countries based on the Hofstede's framework
# correlation based on six metrics: power.distance, individualism, masculinity, uncertainty
# long.term.orientation, and indulgence
library(reshape)
hofstedeDf <- read.csv("hoftstede.csv")
hofstedeDf <- hofstedeDf %>% select(-X) %>%
mutate(power.distance = power.distance / 100,
individualism = individualism / 100,
masculinity = masculinity / 100,
uncertainty = uncertainty / 100,
long.term.orientation = long.term.orientation / 100,
indulgence = indulgence / 100) # scale the hofstede score to 0 - 1
corrMatrix <- cor(t(hofstedeDf[, -1])) # remove the first column with countries
corrMatrix[upper.tri(corrMatrix, diag = TRUE)] <- NA
rownames(corrMatrix) <- colnames(corrMatrix) <- hofstedeDf$country
corrMatrix <- na.omit(reshape::melt(t(corrMatrix)))
corrMatrix <- corrMatrix[ order(corrMatrix$X1, corrMatrix$X2), ]
hofstedeCorr <- corrMatrix %>% select(X1, X2, value) %>% dplyr::rename(countryA=X1, countryB=X2, hofstede.corr=value)
hofstedeCorr$countryA <- as.character(hofstedeCorr$countryA)
hofstedeCorr$countryB <- as.character(hofstedeCorr$countryB)
hofstedeCorr <- transform(hofstedeCorr, countryA = pmin(countryA, countryB), countryB=pmax(countryA, countryB))
head(hofstedeCorr)## countryA countryB hofstede.corr
## 17 India Singapore 0.44105595
## 33 India US -0.40050181
## 49 India Nigeria -0.06628162
## 65 India South.Africa -0.48927712
## 81 Argentina India -0.35943139
## 97 Brazil India 0.18844511# Calculate correlations between sampled survey responses for individuals across pairs of countries
rnormt <- function(n, range, mu, s) {
# range is a vector of two values
F.a <- pnorm(min(range), mean = mu, sd = s)
F.b <- pnorm(max(range), mean = mu, sd = s)
u <- runif(n, min = F.a, max = F.b)
return(qnorm(u, mean = mu, sd = s))
}
countryCorr <- df %>% select(Response.ID, game, country, label, answer)
# Responses that are lower than 5 participants per game
summary <- countryCorr %>% filter(label == "learning_curve") %>%
group_by(country, game) %>%
summarise(n=n(), mean=mean(answer), sd=sd(answer)) %>%
ungroup() %>%
filter(n < 5)
# games that we will ignore altogether
gameTooFew <- unique(summary$game)
gameTooFew## [1] "The Sims 3" "Animal Crossing: New Horizons"
## [3] "Mario Kart 8" "Stardew Valley"
## [5] "Candy Crush"# This function takes a matrix where columns are countries
# This function returns the correlation between countries
# countryA, countryB, label.corr
corr.df <- function(full, label) {
mx <- cor(full, use = "complete.obs")
mx[upper.tri(mx, diag = TRUE)] <- NA
rownames(mx) <- colnames(mx)
mx <- na.omit(reshape::melt(t(mx)))
mx <- mx[ order(mx$X1, mx$X2), ]
labelCorr <- mx %>% select(X1, X2, value)
labelCorr <- labelCorr %>% dplyr::rename(countryA=X1, countryB=X2, label.corr=value)
labelCorr$countryA <- as.character(labelCorr$countryA)
labelCorr$countryB <- as.character(labelCorr$countryB)
labelCorr <- transform(labelCorr, countryA = pmin(countryA, countryB), countryB=pmax(countryA, countryB))
labelCorr$label <- label
return(labelCorr)
}5.1 Game (11) * Label (28) vector correlation vs. Hofstede
Result: All the label correlation are very high, so this might not be what we want
# This step forms a game vector with 11 dimension per label/country with means
gameVector = function(inputrnormtA, inputCountry, inputLabel, lower, upper) {
inputCountry
inputLabel
dfA <- df %>% filter(label == !!inputLabel) %>%
mutate(answer = (1-0) * (answer-!!lower) / (!!upper-!!lower) + 0) %>% # the filter and mutate_at order is important and cannot be switched
filter(country == !!inputCountry) %>%
# filter(!game %in% gameTooFew) %>%
group_by(game) %>%
dplyr::summarise(mean=mean(answer), sd=sd(answer))
filterDf <- df %>% filter(label == !!inputLabel & country == !!inputCountry) %>%
group_by(game) %>%
filter(n() >= 5) %>%
count()
dfA <- dfA %>% mutate(mean=ifelse(!game %in% filterDf$game, NA, dfA$mean))
rnormtA <- inputrnormtA
return(c(rnormtA, c(dfA$mean)))
}countries <- unique(df$country)
labels <- c("control_complexity", "learning_curve", "difficulty", "replayability", "pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space",
"heroic", "real.world", "violent", "action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
output <- data.frame(countryA=c(), countryB=c(), label.corr=c(), label=c())
labelSpace <- data.frame(matrix(ncol=0, nrow=11))
for(label in labels) {
labelSpace <- data.frame(matrix(ncol=0, nrow=11))
for(country in countries) {
v <- c()
if(label %in% c("control_complexity", "learning_curve", "replayability")) {
v <- gameVector(v, country, label, lower=1, upper=3)
} else if (label == "difficulty") {
v <- gameVector(v, country, label, lower=1, upper=4)
} else {
v <- gameVector(v, country, label, lower=0, upper=1)
}
labelSpace <- cbind(labelSpace, as.data.frame(v))
names(labelSpace)[names(labelSpace) == "v"] <- country
}
output <- rbind(labelSpace, output)
}# Plot label correlation against the Hofstede scores
# Create a matrix where columns are countryA, countryB, and label.corr
mx <- cor(labelSpace, use = "pairwise.complete.obs")
mx[upper.tri(mx, diag = TRUE)] <- NA
rownames(mx) <- colnames(mx)
mx <- na.omit(reshape::melt(t(mx)))## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
## caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
## caller; using TRUEmx <- mx[ order(mx$X1, mx$X2), ]
labelCorr <- mx %>% select(X1, X2, value)
labelCorr <- labelCorr %>% dplyr::rename(countryA=X1, countryB=X2, label.corr=value)
labelCorr$countryA <- as.character(labelCorr$countryA)
labelCorr$countryB <- as.character(labelCorr$countryB)
labelCorr <- transform(labelCorr, countryA = pmin(countryA, countryB), countryB=pmax(countryA, countryB))# Plotting the correlation
combined <- hofstedeCorr %>% left_join(labelCorr, by=c("countryA", "countryB"))
ggscatter(combined, x = "hofstede.corr", y = "label.corr",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "Hofstede", ylab = "Label")## `geom_smooth()` using formula 'y ~ x'
5.2 Game (11) vector correlation vs. Hofstede
This one compare a 11-dimensional vector between countries for each label. So each dot is a comparison between two countries for a label (e.g., US vs Japan for control_complexity)
updateVector = function(inputCountry, inputLabel, lower=0, upper=1) {
inputCountry
inputLabel
dfA <- df %>% filter(label == {{ inputLabel }}) %>%
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # the filter and mutate_at order is important and cannot be switched
filter(country == {{ inputCountry }}) %>%
filter(!game %in% gameTooFew) %>%
group_by(game) %>%
dplyr::summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE))
rnormtA <- c()
for(row in 1:nrow(dfA)) {
meanA <- as.double(dfA[row, "mean"])
sdA <- as.double(dfA[row, "sd"])
rnormtA <- c(rnormtA, meanA)
}
return(rnormtA)
}countries <- unique(df$country)
labels <- c("control_complexity", "learning_curve", "difficulty", "replayability", "pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space",
"heroic", "real.world", "violent", "action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
output <- data.frame(countryA=c(), countryB=c(), label.corr=c(), label=c())
for(label in labels) {
v <- c()
labelSpace <- data.frame(matrix(ncol = 0, nrow = 600))
for(country in countries) {
if(label %in% c("control_complexity", "learning_curve", "replayability")) {
v <- updateVector(country, label, lower=1, upper=3)
} else if (label == "difficulty") {
v <- updateVector(country, label, lower=1, upper=4)
} else {
v <- updateVector(country, label, lower=0, upper=1)
}
labelSpace <- cbind(labelSpace, as.data.frame(v))
names(labelSpace)[names(labelSpace) == "v"] <- country
}
#break
output <- rbind(corr.df(labelSpace, label), output)
}# Now plotting the two correlations
combined <- hofstedeCorr %>% left_join(output, by=c("countryA", "countryB"))
ggscatter(combined, x = "hofstede.corr", y = "label.corr",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "Hofstede", ylab = "Label")## `geom_smooth()` using formula 'y ~ x'
5.3 Sampling approach 1
For each country and label, we sample 100 points based on mean and sd. Overall, each dot is a 6-dimensional game vector for each label between two countries. (control_complexity: [PUBG, …])
updateSingleVector = function(inputCountry, inputLabel, lower=0, upper=1) {
inputCountry
inputLabel
dfA <- df %>% filter(label == {{ inputLabel }}) %>%
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # the filter and mutate_at order is important and cannot be switched
filter(country == {{ inputCountry }}) %>%
filter(!game %in% gameTooFew) %>%
group_by(game) %>%
dplyr::summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE))
rnormtA <- c()
for(row in 1:nrow(dfA)) {
meanA <- as.double(dfA[row, "mean"])
sdA <- as.double(dfA[row, "sd"])
gameA = meanA
if(sdA == 0) {
gameA <- rep(meanA, 100)
} else {
gameA <- rnormt(100, c(0, 1), meanA, sdA)
}
rnormtA <- c(rnormtA, gameA)
}
return(rnormtA)
}countries <- unique(df$country)
labels <- c("control_complexity", "learning_curve", "difficulty", "replayability", "pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space",
"heroic", "real.world", "violent", "action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
output <- data.frame(countryA=c(), countryB=c(), label.corr=c(), label=c())
for(label in labels) {
v <- c()
labelSpace <- data.frame(matrix(ncol = 0, nrow = 600))
for(country in countries) {
if(label %in% c("control_complexity", "learning_curve", "replayability")) {
v <- updateSingleVector(country, label, lower=1, upper=3)
} else if (label == "difficulty") {
v <- updateSingleVector(country, label, lower=1, upper=4)
} else {
v <- updateSingleVector(country, label, lower=0, upper=1)
}
labelSpace <- cbind(labelSpace, as.data.frame(v))
names(labelSpace)[names(labelSpace) == "v"] <- country
}
#break
output <- rbind(corr.df(labelSpace, label), output)
}# Now plotting the two correlations
combined <- hofstedeCorr %>% left_join(output, by=c("countryA", "countryB"))
ggscatter(combined, x = "hofstede.corr", y = "label.corr",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "spearman",
xlab = "Hofstede", ylab = "Label")## `geom_smooth()` using formula 'y ~ x'
5.4 Sampling 2: Game (11) * Label (28) vector correlation + sampling
Each vector is 11 games * 28 labels * 100
# Compare "label space" vector
updateVector <- function(inputCountry, inputrnormtA, inputLabel, lower=0, upper=1) {
# update the vector of a country based on a specific label
# Args:
# country: string of countries
# label: string of the label of interest
# lower, upper: the lower and upper bound (0-1 for binary, 0-3 for control_complexity etc, 0-4 for difficulty)
# Returns:
# an updated vector in the label space
inputLabel
inputCountry
if(upper - lower == 0) {
print("Something happened")
}
dfA <- df %>% filter(label == {{ inputLabel }}) %>%
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # the filter and mutate_at order is important and cannot be switched
filter(country == {{ inputCountry }}) %>%
filter(!game %in% gameTooFew) %>%
group_by(game) %>%
dplyr::summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE))
rnormtA <- inputrnormtA
for(row in 1:nrow(dfA)) {
meanA <- as.double(dfA[row, "mean"])
sdA <- as.double(dfA[row, "sd"])
if(sdA == 0) {
gameA <- rep(meanA, 100)
} else {
gameA <- rnormt(100, c(0, 1), meanA, sdA)
}
rnormtA <- c(rnormtA, gameA)
}
return(rnormtA)
}
labelSpace <- data.frame(matrix(ncol = 0, nrow = 28*600))
countries <- unique(df$country)
labels <- c("control_complexity", "learning_curve", "difficulty", "replayability", "pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space", "heroic", "real.world", "violent",
"action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
for(country in countries) {
v <- c()
for(label in labels) {
if(label %in% c("control_complexity", "learning_curve", "replayability")) {
v <- updateVector(country, v, label, lower=1, upper=3)
} else if (label == "difficulty") {
v <- updateVector(country, v, label, lower=1, upper=4)
} else {
v <- updateVector(country, v, label, lower=0, upper=1)
}
}
labelSpace <- cbind(labelSpace, as.data.frame(v))
names(labelSpace)[names(labelSpace) == "v"] <- country
}library(corrplot)
mx <- cor(labelSpace, use = "pairwise.complete.obs")
corrplot(mx, type = "upper", order = "FPC", tl.cex=2,
tl.col = "black", tl.srt = 45)
# Plot label correlation against the Hofstede scores
mx[upper.tri(mx, diag = TRUE)] <- NA
rownames(mx) <- colnames(mx)
mx <- na.omit(reshape::melt(t(mx)))
mx <- mx[ order(mx$X1, mx$X2), ]
labelCorr <- mx %>% select(X1, X2, value)
labelCorr <- labelCorr %>% dplyr::rename(countryA=X1, countryB=X2, label.corr=value)
labelCorr$countryA <- as.character(labelCorr$countryA)
labelCorr$countryB <- as.character(labelCorr$countryB)
labelCorr <- transform(labelCorr, countryA = pmin(countryA, countryB), countryB=pmax(countryA, countryB))
head(labelCorr)## countryA countryB label.corr
## 17 India US 0.3071151
## 33 Singapore US 0.3100672
## 49 South.Africa US 0.3755622
## 65 Nigeria US 0.2735908
## 81 Argentina US 0.3725302
## 97 Brazil US 0.3708676- This finding shows that there is no clear trend in the correlation between Hofstede’s dimensions of culture and the label correlations. It seems that the label correlation is roughly the same across these countries. In other words, the Hofstede’s dimensions of culture, which was observed in organizational culture at IBM, doesn’t seem to explain the difference well.
# Now plotting the two correlations
combined <- hofstedeCorr %>% left_join(labelCorr, by=c("countryA", "countryB"))
ggplot(aes(x=hofstede.corr, y=label.corr), data=combined) +
geom_point() +
ylim(c(0,1)) +
geom_smooth(method='lm')## `geom_smooth()` using formula 'y ~ x'
cor(combined$hofstede.corr, combined$label.corr, method = "spearman")## [1] 0.0307313The following takes a similar approach, but looks at the individual game level.
6 Figure 4: How similar are label choices for bilingual speakers when asked questions in their native language versus English?
library(tidyverse)
library(tidytext)
library(data.table)
source("read.R")
theme_set(
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8))
)
df <- df %>% filter(!label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art"))We ran our surveys in both English and non-English languages on bilingual speakers within each non-English speaking country.
rnormt <- function(n, range, mu, s) {
if(s == 0) {
return(c(NaN))
}
# range is a vector of two values
F.a <- pnorm(min(range), mean = mu, sd = s)
F.b <- pnorm(max(range), mean = mu, sd = s)
u <- runif(n, min = F.a, max = F.b)
return(qnorm(u, mean = mu, sd = s))
}# For some weird reason, I need to output the first four variable
compareLanguage <- function(languageA, languageB, inputCountries, label, lower=0, upper=1) {
languageA
languageB
inputCountries
label
dfA = df %>% dplyr::filter(label == {{ label }}) %>% # select the label
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # rescale the Likert scale answer to 0-1 (e.g., 1-3, 1-4 to 0-1)
dplyr::filter(Language == {{ languageA }} & country %in% inputCountries) %>%
group_by(game) %>%
summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE)) %>%
filter(!is.na(sd)) # in case there is only one response in a country
# same operation for languageB
dfB = df %>% dplyr::filter(label == {{ label }}) %>% # select the label
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # rescale the Likert scale answer to 0-1 (e.g., 1-3, 1-4 to 0-1)
dplyr::filter(Language == {{ languageB }} & country %in% inputCountries) %>%
group_by(game) %>%
summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE)) %>%
filter(!is.na(sd)) # in case there is only one response in a country
rnormtA <- c()
rnormtB <- c()
for(row in unique(df$game)) {
meanA <- as.double(dfA[dfA$game == row, "mean"])
meanB <- as.double(dfB[dfB$game == row, "mean"])
sdA <- as.double(dfA[dfA$game == row, "sd"])
sdB <- as.double(dfB[dfB$game == row, "sd"])
if(any(is.na(meanA)) | any(is.na(meanB))) next;
if(any(is.na(sdA)) | any(is.na(sdB))) next;
if(any(sdA == 0) | any(sdB == 0)) next;
gameA <- rnormt(1000, c(0, 1), meanA, sdA)
gameB <- rnormt(1000, c(0, 1), meanB, sdB)
rnormtA <- c(rnormtA, gameA)
rnormtB <- c(rnormtB, gameB)
}
# no game is available
if(length(rnormtA) == 0) {
return(data.frame(languageA = languageA, languageB = languageB, estimate=NaN, p=NaN, label=label))
}
cor.res = cor.test(rnormtA, rnormtB, method=c("pearson"), use = "complete.obs")
return(data.frame(languageA = languageA, languageB = languageB, estimate=cor.res$estimate, p=cor.res$p.value, label=label))
}matrix <- combn(unique(df$Language), 2) # get the combination of languages
new.df <- data.frame(languageA=c(), languageB=c(), estimate=c(), p=c(), label=c())
for(col in 1:ncol(matrix)) {
# for each combination (English vs. language X
l1 <- matrix[1, col] # English
if(l1 != "English") break
l2 <- matrix[2, col] # language X
language.countries <- df %>% filter(Language == l2) # find the correpondings countries to language X
for(label in c("control_complexity", "learning_curve", "replayability")) {
comparison <- compareLanguage(l1, l2, c(unique(language.countries$country)), label, lower=1, upper=3)
new.df <- rbind(new.df, comparison)
}
comparison <- compareLanguage(l1, l2, c(unique(language.countries$country)), "difficulty", lower=1, upper=4)
new.df <- rbind(new.df, comparison)
for(label in labels[!labels %in% c("control_complexity", "learning_curve", "difficulty", "replayability")]){
comparison <- compareLanguage(l1, l2, c(unique(language.countries$country)), label, lower=0, upper=1)
new.df <- rbind(new.df, comparison) # update the new.df
}
}- This result looks very cool as it indicates that the current translation of tags might be leaning toward popular markets. Japanese, Arabic, Greek markets are poorly translated.
new.df %>% filter(estimate < 0)## languageA languageB estimate p label
## cor81 English German -0.0015186309 0.87346612 achievement
## cor88 English Greek -0.0028003688 0.87814680 pacifist
## cor90 English Greek -0.0127911144 0.28460398 cozy
## cor94 English Greek -0.0036384955 0.79701126 real.world
## cor97 English Greek -0.0032613144 0.81766231 emotional
## cor101 English Greek -0.0024696280 0.84832425 grinding
## cor103 English Greek -0.0103806079 0.42143593 hand.drawn
## cor108 English Greek -0.0052058916 0.64153084 achievement
## cor109 English Greek -0.0151299980 0.28477862 immersion
## cor110 English Greek -0.0084885782 0.64210821 creativity
## cor113 English Japanese -0.0193274718 0.22166651 replayability
## cor115 English Japanese -0.0720159174 0.02275921 pacifist
## cor116 English Japanese -0.0234320142 0.29491504 made.for.kids
## cor117 English Japanese -0.0082740802 0.71152920 cozy
## cor118 English Japanese -0.0108711495 0.55170523 zen
## cor120 English Japanese -0.0190434275 0.39466231 heroic
## cor124 English Japanese -0.0063587958 0.84082892 emotional
## cor131 English Japanese -0.0023162515 0.91754925 action.motivation
## cor132 English Japanese -0.0008932378 0.96099559 social
## cor133 English Japanese -0.0454120321 0.04228891 mastery
## cor134 English Japanese -0.0261195416 0.24298013 achievement
## cor136 English Japanese -0.0132795981 0.40110463 creativity
## cor154 English Polish -0.0147580221 0.25304875 grinding
## cor155 English Polish -0.0246806752 0.17654853 hand.drawn
## cor158 English Polish -0.0093197112 0.47043775 social
## cor163 English Arabic -0.0171182883 0.58872101 control_complexity
## cor164 English Arabic -0.0154268056 0.62607602 learning_curve
## cor166 English Arabic -0.0584431924 0.06468865 difficulty
## cor169 English Arabic -0.0022168611 0.94418115 violent
## cor170 English Arabic -0.0368267869 0.24462532 emotional